A
AI Agent A software system powered by an AI model that can take actions autonomously — browsing the web, writing files, sending emails, running code — rather than just responding to questions. Unlike a chatbot, an agent can work through multi-step tasks without a human directing each step. See: AI Agents for Small Business: The Complete Guide. Agentic AI AI that operates with a degree of autonomy — planning, taking actions, and adjusting based on results — rather than waiting for a human to prompt each step. Agentic AI is what makes true automation possible. See: What is Agentic AI?
API (Application Programming Interface) A way for software systems to talk to each other. When a business uses an AI model via API, they're connecting to the model's capabilities programmatically — embedding it in their own tools and workflows rather than accessing it through a chat interface. Automation Having a system perform a task without manual human intervention each time. AI adds the ability to handle variable, language-heavy inputs that traditional automation tools couldn't process. See: How to Automate Business Workflows with AI. C
Claude The AI model developed by Anthropic. Available as a chat assistant (claude.ai), a desktop agent (Claude Cowork), and a developer tool (Claude Code). Our primary recommendation for small business use cases due to its strength in complex reasoning, long documents, and following multi-step instructions reliably. Claude Code An agentic coding and automation tool from Anthropic that runs in your terminal or development environment. Used to build and deploy custom AI workflows, integrations, and automations. See: What is Claude Code? Claude Cowork Anthropic's desktop AI agent. Unlike the browser version of Claude, Cowork runs on your computer and can interact directly with your files, applications, and local tools. See: What is Claude Cowork?
Context Window The amount of text an AI model can read and reason about in a single interaction. A larger context window means the model can process longer documents, more conversation history, or larger data sets at once. E
Embeddings A way of representing text as numbers that capture semantic meaning. Used in RAG systems and search tools to find relevant information based on meaning rather than exact keyword match — the backend of AI-powered knowledge base tools. F
Fine-tuning Further training an existing AI model on your own data to make it better at a specific task. Often discussed, rarely necessary for most SMB use cases. Well-engineered prompts and RAG systems solve most business problems without the complexity and cost of fine-tuning.
Foundation Model A large AI model trained on broad data that serves as the base for many applications. Claude, GPT-4o, and Gemini are all foundation models. Businesses typically interact with them through chat interfaces, APIs, or agent tools built on top. G
Generative AI AI that produces new content — text, images, code, audio — rather than just classifying or analyzing existing content. When a business uses AI to draft emails, generate product descriptions, or summarize documents, they're using generative AI. GPT-4o OpenAI's flagship AI model as of 2026. Available through ChatGPT and the OpenAI API. Has the broadest third-party integration ecosystem of any current model. See: Claude vs. ChatGPT vs. Gemini for Business.
Guardrails Constraints built into an AI workflow to limit what it can do, what it will say, or how it handles edge cases. Guardrails ensure the AI stays within defined boundaries — not drafting responses outside its scope, not processing data it shouldn't access. H
Hallucination When an AI model generates information that sounds plausible but is factually incorrect. Managed in business workflows by keeping the AI working from provided documents rather than relying on training knowledge — and by building in human review where accuracy is critical.
Human-in-the-Loop An automation design where a human reviews, approves, or corrects AI output before it becomes final or triggers an action. Most business AI workflows should be human-in-the-loop, at least initially. I
Integration A connection between two software systems that allows them to share data or trigger actions. In AI automation, integrations allow an AI to read from your CRM, write to your spreadsheet, send emails, or pull from your e-commerce dashboard. L
LLM (Large Language Model) The underlying technology behind modern AI chat and generation tools. Trained on large amounts of text data and learns to predict and generate language. Claude, GPT-4o, and Gemini are all LLMs. M
MCP (Model Context Protocol) An open standard developed by Anthropic that defines how AI models connect to external tools and data sources. Makes it easier to build integrations between Claude and other software in a consistent, interoperable way.
Multimodal AI AI that can process multiple types of input — text, images, audio, video — rather than text alone. Current business applications include reading documents with embedded images, analyzing charts, and processing voice memos. n
n8n An open-source workflow automation platform. Often used to connect AI models to other business tools — CRMs, email platforms, databases, spreadsheets. A common infrastructure layer in custom AI automation builds. O
Orchestration The coordination of multiple AI agents or workflow steps toward a larger goal. Orchestration turns individual AI capabilities into end-to-end business workflows that run reliably from trigger to output. P
Prompt The instruction or input given to an AI model. In a business workflow, a prompt is a carefully designed template that tells the AI what to do, what context to use, what format to produce, and what constraints to observe.
Prompt Engineering The practice of designing and refining prompts to get reliable, high-quality outputs from an AI model. Mostly a matter of being specific, providing good examples, and testing systematically — not a mystical skill. R
RAG (Retrieval-Augmented Generation) A technique that allows an AI model to answer questions using your specific documents and data, rather than relying only on its training knowledge. Lets you build an AI that answers questions about your business using your actual documentation. ROI (Return on Investment) In the AI context: the measurable value generated by an AI implementation relative to what it cost to build and run. Typically measured as hours saved per week multiplied by the cost of that time, minus implementation costs. See: The Real ROI of AI Consulting for Small Business. S
System Prompt A set of instructions given to an AI model before a conversation begins, defining its role, tone, constraints, and behavior. The system prompt is how you configure AI to act like your customer service rep, your document analyst, or your report writer. T
Token The unit AI models use to process text. Roughly 4 characters or 0.75 words in English. Tokens determine context window limits and API costs (you pay per token processed). For most business workflows, token costs are small relative to value generated.
Tool Use The ability of an AI model to call external tools — search the web, run code, read a file, query a database — as part of generating a response. Tool use is what allows an agent to take actions in the world rather than just produce text.
Trigger The event that starts an automated workflow. Common triggers: a new email arrives, a form is submitted, a file lands in a folder, a scheduled time is reached, a new row is added to a spreadsheet. V
Vector Database A database designed to store and search embeddings. Used in RAG systems to find the most relevant documents or passages in response to a query — the backend infrastructure behind AI-powered search and knowledge base tools. W
Webhook A way for one software system to notify another when something happens in real time. Webhooks are often used as triggers in AI automation: when a new lead is created in the CRM, a webhook fires and starts the AI workflow.
Workflow A defined sequence of steps that transforms an input into an output. A well-defined workflow is the prerequisite for any reliable automation — the AI can only execute what it's been given a clear process for. Z
Zero-Shot Asking an AI to perform a task without providing examples. Contrast with few-shot prompting, where you include 2–3 examples of the desired output. Few-shot almost always produces more consistent results for business workflows. Ready to Put These Terms to Work?
Understanding the terminology is step one. If you're ready to figure out which of these technologies belongs in your business, start with a free review.
Book a Free AI Review